Change Detection in Overhead Imagery Using Neural Networks
Applied Intelligence
Detecting Changes of Buildings from Aerial Images Using Shadow and Shading Model
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Modeling satellite image streams for change analysis
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
International Journal of Remote Sensing
Objects based change detection in a pair of gray-level images
Pattern Recognition
IEEE Transactions on Image Processing
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
| Hi-index | 0.01 |
A new approach to wide area surveillance is described that is based on the detection and analysis of changes across two or more images over time. Methods for modeling and detecting general patterns of change associated with construction and other kinds of activities that can be observed in remotely sensed imagery are presented. They include a new nonlinear prediction technique for measuring changes between images and temporal segmentation and filtering techniques for analyzing patterns of change over time. These methods are applied to the problem of detecting facility construction using Landsat Thematic Mapper imagery. Full scene results show the methods to be capable of detecting specific patterns of change with very few false alarms. Under all conditions explored, as the number of images used increases, the number of false alarms decreases dramatically without affecting the detection performance. It is argued that the processing gain that results in using more than two images justifies the increased computational complexity and storage requirements of our approach over single image object detection and conventional change detection techniques